Urban green and blue space changes: A spatiotemporal evaluation of impacts on ecosystem service value in Bangladesh

Evaluating the role of formal urban blue spaces in ecosystem service provision: Insights from New Town, Kolkata

The Urban Blue Space (UBS) refers to managed and unmanaged water spaces crucial for sustainable urban development. This study examines the changes in Ecosystem Service Value (ESV) associated with UBS change in New Town, Kolkata from 1990 to 2020. This study reveals a 254-ha decrease in total blue space and a significant transformation from informal to formal UBS. A 438.43 % increase in built-up areas causes fragmentation of the overall blue spaces, whereas the core area has increased for formal blue spaces providing higher ESV. The total Ecosystem Service Value (ESV) for formal UBS has increased from $478070.44 per year/hectare to $3460197.99 per year/hectare during the study time showing a growing contribution of formal blue space for sustaining ecosystem services. However, the ESV from informal UBS has decreased from $10523635.78 per year/hectare to $9230323.36 per year/hectare. This positive change in ESV obtained from formal UBS and negative change in ESV obtained from informal UBS indicates the importance of formal UBS for maintaining ESV supply for the study area. Hence, the findings highlight the need for policy formulation for the conservation of existing formal blue spaces and the upgradation from informal UBS to formal UBS to maintain sustainable urban development of the study area. This new categorization of UBS and the impact of formal and informal UBS fragmentation on ESV change will open a new avenue for understanding the importance of formal UBS for maintaining ESV in the changing urban scenario of other cities of the world.

Identifying the spatio-temporal evolution and driving mechanisms of ecosystem service value in high groundwater table coal mining areas

In coal mining areas with high groundwater tables, surface subsidence has emerged as a non-negligible phenomenon, stemming from long-term coal mining activities. Employing the Huainan mining area as an exemplar, this research meticulously examines the temporal and spatial attributes of ecosystem service value (ESV) across distinct timeframes of 2005, 2010, 2015, and 2020, utilizing the refined equivalent factor approach in conjunction with spatial analysis methodologies. To delve into the primary forces driving the observed changes, the optimal parameter-based geographical detector (OPGD) model is subsequently utilized as a tool for analysis. Lastly, the study delves into the trade-offs and synergies existing between four exemplary services at the grid level, utilizing Spearman correlation coefficient and bivariate spatial autocorrelation. The findings suggest that: (1) From 2005 to 2020, the total ESV in the Huainan mining area demonstrated a general increasing tendency, primarily attributed to the increase in waters. (2) Throughout the research period, the ecosystem service functions in the coal mining area all exhibited relatively significant hydrological regulation and waste treatment capabilities. (3) Vegetation factors significantly influenced the ESV in the Huainan mining area. (4) The Huainan mining area predominantly exhibited synergistic effects among ecosystem services, with the most pronounced synergy occurring between cultural services (CS) and regulating services (RS). All services were transitioning towards an enhanced trend of synergistic effects. (5) Significant spatial variations are present in the observed trade-offs and synergies among diverse ecosystem services. The aforementioned research findings will provide scientific theoretical guidance for rational mining activities and ecological environmental governance in coal mining areas.

Research on the coupling coordination, spatiotemporal evolution and zoning management of high-quality development and ecosystem service value in China

High-quality development (HQD) and ecosystem service value (ESV) are among the most prominent topics in China today. Understanding the dynamic coupling and coordination relationship between HQD and ESV is significant for achieving global urban sustainability goals. This study breaks the traditional paradigm of economic and ecological coupling coordination research. By measuring HQD and ESV in 292 Chinese cities from 2000 to 2021, we construct a coupling coordination degree (CCD) model to profoundly investigate the changes, coupling coordination, and underlying reasons of HQD and ESV from theoretical and spatiotemporal perspectives. The results indicate that: (1) HQD in Chinese cities has undergone phased changes and has shown significant improvement, with evident agglomeration characteristics centered around urban clusters. (2) ESV has a significant distribution characteristic among cities: low in the eastern coastal areas and high in the inland areas, which is negatively correlated with GDP. (3) The CCD of Chinese cities has undergone phased changes and exhibits a clear coordination trend. Spatially, the CCD is relatively lower in eastern coastal cities and higher in northeastern and southwestern cities. (4) By combining spatial autocorrelation and CCD results, Chinese cities are classified into four categories: Green innovation, resource optimization, development potential, and ecological protection.

Combining deep learning and machine learning techniques to track air pollution in relation to vegetation cover utilizing remotely sensed data

The rapid urban expansion in Dhaka, the capital of Bangladesh, has escalated air pollution levels and led to a significant decrease in green spaces. This study employed machine learning (ML) and deep learning (DL) techniques to examine the relationship between rising concentrations of particulate matter (PM2.5 and PM10) and decreasing urban green spaces from 1990 to 2022. The ML algorithms, specifically XGB, SVM, and RF, effectively predicted high air pollution areas, while DL models Unet, Unet++, MAnet, and Linknet accurately forecasted vegetation cover trends. The findings confirm a strong negative correlation between increased air pollution and vegetation. The decline in green spaces is not only a local concern but also has broader regional implications due to the transboundary nature of air pollution. The results highlight the critical need for pollution management strategies and urban planning that prioritize green infrastructure. The study also emphasizes the value of using ML and DL techniques for accurate, data-driven environmental assessments and predictions. Future studies could incorporate high-resolution images and integrate socioeconomic data to achieve a more comprehensive perspective on the urban environmental challenges faced by rapidly developing cities like Dhaka. The use of an integrated ML and DL strategy as highlighted in this research appears to be a practical and economical method for tracking vegetation degradation and change, and in establishing the causal links to air pollution.

Spatial relationship between carbon emissions and ecosystem service value based on land use: A case study of the Yellow River Basin

Land use changes significantly impact both carbon emissions and ecosystem service value (ESV). However, few studies have been conducted on the spatial relationship between land use carbon emissions (LUCE) and ESV. Thus, focused on the Yellow River Basin (YRB), this study independently calculates carbon emissions from land use change (LUCE) and ecosystem service values (ESV) in the region. Utilizing spatial autocorrelation methods, we analyze the spatiotemporal pattern of LUCE and ESV and subsequently apply the bivariate spatial autocorrelation method to explore their spatial relationship. The results prove that: (1) The YRB's LUCE has continuously increased, with construction land acting as the dominant carbon source and woodland acting as the main carbon sink. The LUCE in the YRB had a positive spatial autocorrelation. (2) The YRB's ESV increased. Spatially, the ESV in the YRB showed a positive autocorrelation. (3) Both LUCE and ESV exhibited negative spatial autocorrelation, with predominant patterns of bivariate localized spatial autocorrelation identified as High-Low agglomeration (H-L) and Low-High agglomeration (L-H). Cities with the L-H pattern were primarily located in Qinghai Province and Inner Mongolia. In contrast, cities with the H-L pattern were mainly observed in the western section of Shandong and the northeastern region of Henan. The study revealed the negative impact of increased carbon emissions from land use on the value of ecosystem services, providing assistance in the development of relevant environmental policies and promoting sustainable development in the YRB.

A new method for evaluating the coordinated relationship between vegetation greenness and urbanization

Understanding and measuring the link between vegetation greenness and urbanization is crucial for public health and sustainable development. However, previous methods may oversimplify urbanization indicators and fail to adequately reflect changes in their relationships. To address this, we introduced a comprehensive urbanization vegetation coordination index (CUVCI) on the basis of comprehensive urbanization and a compound annual growth rate and applied this index to the Yellow River Basin in China. We examined the spatiotemporal evolution of the NDVI, comprehensive urbanization level (CUL), and CUVCI from 2000 to 2019 and explored potential driving factors. The results indicate that: (1) from 2000 to 2019, 87.8% of the areas in the Yellow River Basin demonstrated a trend of vegetation growth, with growth levels highest in the midstream regions, followed by the upstream and downstream areas. (2) The CULs of most cities in the basin have shown an increasing trend, with the CUL levels in the middle and downstream cities being significantly higher than those in the upstream cities. (3) CUVCI from 2000 to 2019 was characterized mainly by general coordination (57.4%) and minor conflict (18.5%), with minor-conflict cities located primarily in the middle and lower basin. The coordinating relationships in most cities show signs of improvement. (4) While natural environmental factors such as precipitation, temperature, and relief have a significant impact on CUVCI, scientific researchers can promote the coordinated development of vegetation greenness and urbanization. Our findings suggest that cities in the basin are experiencing economic prosperity and increased greenery. However, strong- and minor-conflict cities should prioritize cultivating and attracting scientific research talent and learning from coordinated provincial capital cities.

Dynamic simulation and projection of ESV changes in arid regions caused by urban growth under climate change scenarios

Spatial simulation and projection of ecosystem services value (ESV) changes caused by urban growth are important for sustainable development in arid regions. We developed a new model of cellular automata based grasshopper optimization algorithm (named GOA-CA) for simulating urban growth patterns and assessing the impacts of urban growth on ESV changes under climate change scenarios. The results show that GOA-CA yielded overall accuracy exceeding 98%, and FOM for 2010 and 2020 were 43.2% and 38.1%, respectively, indicating the effectiveness of the model. The prairie lost the highest economic ESVs (192 million USD) and the coniferous yielded the largest economic ESV increase (292 million USD) during 2000-2020. Using climate change scenarios as urban future land use demands, we projected three scenarios of the urban growth of Urumqi for 2050 and their impacts on ESV. Our model can be easily applied to simulating urban development, analyzing its impact on ESV and projecting future scenarios in global arid regions.

Urban river recovery: a systematic review on the effectiveness of water clean-up programs

Urban rivers are affected at different levels by the intensification of human activities, representing a serious threat to the maintenance of terrestrial life and sustainable urban development. Consequently, great efforts have been dedicated to the ecological restoration of urban rivers around the world, as a solution to recovering the environmental functionality of these environments. In this sense, the present work aimed to investigate the effectiveness of interventions carried out aimed at the recovery of urban rivers, through a systematic review of the literature between 2010 and 2022, using the search term "rivers recovery." The results showed that there have been notable advances in the implementation of river recovery programs in urban areas around the world between the years analyzed. The ecosystems studied were affected, for the most part, by the increase in the supply of nutrients from domestic and industrial effluents, in addition to having highly urbanized surroundings and with several changes in land use patterns. The preparation of this literature review made it possible to demonstrate that the effectiveness of river recovery is extremely complex, since river recovery projects are developed for different reasons, as well as being carried out in different ways according to the intended objective.

Past dynamics and future prediction of the impacts of land use cover change and climate change on landscape ecological risk across the Mongolian plateau

Land use/land cover (LULC) change and climate change are interconnected factors that affect the ecological environment. However, there is a lack of quantification of the impacts of LULC change and climate change on landscape ecological risk under different shared socioeconomic pathways and representative concentration pathways (SSP-RCP) on the Mongolian Plateau (MP). To fill this knowledge gap and understand the current and future challenges facing the MP's land ecological system, we conducted an evaluation and prediction of the effects of LULC change and climate change on landscape ecological risk using the landscape loss index model and random forest method, considering eight SSP-RCP coupling scenarios. Firstly, we selected MCD12Q1 as the optimal LULC product for studying landscape changes on the MP, comparing it with four other LULC products. We analyzed the diverging patterns of LULC change over the past two decades and observed significant differences between Mongolia and Inner Mongolia. The latter experienced more intense and extensive LULC change during this period, despite similar climate changes. Secondly, we assessed changes in landscape ecological risk and identified the main drivers of these changes over the past two decades using a landscape index model and random forest method. The highest-risk zone has gradually expanded, with a 30% increase compared to 2001. Lastly, we investigated different characteristics of LULC change under different scenarios by examining future LULC products simulated by the FLUS model. We also simulated the dynamics of landscape ecological risks under these scenarios and proposed an adaptive development strategy to promote sustainable development in the MP. In terms of the impact of climate change on landscape ecological risk, we found that under the same SSP scenario, increasing RCP emission concentrations significantly increased the areas with high landscape ecological risk while decreasing areas with low risk. By integrating quantitative assessments and scenario-based modeling, our study provides valuable insights for informing sustainable land management and policy decisions in the region.

Modelling vegetation land fragmentation in urban areas of Western Province, Sri Lanka using an Artificial Intelligence-based simulation technique

Vegetation land fragmentation has had numerous negative repercussions on sustainable development around the world. Urban planners are currently avidly investigating vegetation land fragmentation due to its effects on sustainable development. The literature has identified a research gap in the development of Artificial Intelligence [AI]-based models to simulate vegetation land fragmentation in urban contexts with multiple affecting elements. As a result, the primary aim of this research is to create an AI-based simulation framework to simulate vegetation land fragmentation in metropolitan settings. The main objective is to use non-linear analysis to identify the factors that contribute to vegetation land fragmentation. The proposed methodology is applied for Western Province, Sri Lanka. Accessibility growth, initial vegetation large patch size, initial vegetation land fragmentation, initial built-up land fragmentation, initial vegetation shape irregularity, initial vegetation circularity, initial building density, and initial vegetation patch association are the main variables used to frame the model among the 20 variables related to patches, corridors, matrix and other. This study created a feed-forward Artificial Neural Network [ANN] using R statistical software to analyze non-linear interactions and their magnitudes. The study likewise utilized WEKA software to create a Decision Tree [DT] modeling framework to explain the effect of variables. According to the ANN olden algorithm, accessibility growth has the maximum importance level [44] between -50 and 50, while DT reveals accessibility growth as the root of the Level of Vegetation Land Fragmentation [LVLF]. Small, irregular, and dispersed vegetation patches are especially vulnerable to fragmentation. As a result, study contributes detech and managing vegetation land fragmentation patterns in urban environments, while opening up vegetation land fragmentation research topics to AI applications.

Predicting future coastal land use/cover change and associated sea-level impact on habitat quality in the Northwestern Coastline of Guinea-Bissau

The assessment of coastal land use/cover (LULC) change is one of the most precise techniques for detecting spatio-temporal change in the coastal system. This study, integrated Land Change Modeler, Habitat Quality Model, and Digital Shoreline Analysis System, to quantify spacio-temporal coastal LULC change and driving forces between 2000 and 2020. Combined the CA-Markov Model with Sea Level Affecting Marshes Model (SLAMM), merged local SLR data with future representative concentration pathway (RCP8.5) scenarios, and predicted future coastal LULC change and associated sea-level rise (SLR) impact on the coastal land use and habitat quality in short-, medium- and long-term. The study area had significant coastal LULC change between 2000 and 2020. The tidal flats, whose change was driven mainly by sea level, registered a total net gain of 57.93 km². We also observed the significant loss of developed land whose change was influenced by tidal flat with a total loss of -75.58 km². The tidal flat will experience a stunning net gain of 80.55 km² between 2020 and 2060, making developed land the most negatively impacted land in the study area. The study led to the conclusion that the uncontrolled conversion of saltmarshes, mixed-forest, and mangroves into agriculture and infrastructures were the main factors affecting the coastal systems, including the faster coastal erosion and accretion observed during a 20-year period. The study also concluded that a low coastal elevation of -1 m and a slope of less than 2° have contributed to coastal change. Unprecedented changes will unavoidably pose a danger to coastal ecological services, socioeconomic growth, and food security. Timely efforts should be made by establishing sustainable mitigation methods to avoid the future impact.

Habitat quality dynamics in China's first group of national parks in recent four decades: Evidence from land use and land cover changes

As the most biodiversity-rich part of the protected areas system, habitats within the pilot national parks have long been threatened by drastic human-induced land use and land cover changes. The growing concern about habitat loss has spurred China's national park project to shift from pilot to construction phase with the official establishment of China's first group of national parks (CFGNPs) in October 2021. But far too little attention has been paid to the synergistic work concerning the habitat quality (HQ) dynamics of all five national parks. Here, the InVEST model, combined with a satellite-derived land use and land cover product and a hot spot analysis (HSA) method, was used to investigate the HQ dynamics at the park- and pixel-scale within the CFGNPs. Our results demonstrate that the past ecological conservation practices within national parks have been unpromising, especially in Giant Panda National Park, Northeast China Tiger and Leopard National Park (NCTL), and Wuyi Mountain National Park (WYM), where HQ as a whole showed a significant decline. Furthermore, more than half of Hainan Tropical Rainforest National Park (87.2%), WYM (77.4%), and NCTL (52.9%) showed significant HQ degradation from 1980 to 2019. Besides, increasing trends in the area shares of HQ degraded pixels were observed in all five national parks from 1980-1999 to 2000-2019. The HSA implied that the hot spots of high HQ degradation rates tend to occur in areas closer to urban settlements or on the edge of national parks, where human activities are intensive. Despite these disappointing findings, we highlighted from the observed local successes and the HQ plateau that the construction of CFGNPs is expected to reverse the deteriorating HQ trends. Thus, we concluded our paper by proposing an HSA-based regulatory zoning scheme that includes five subzones to guide the future construction of China's national park system.

Multi-Scenario Simulation to Predict Ecological Risk Posed by Urban Sprawl with Spontaneous Growth: A Case Study of Quanzhou

The rapid expansion of different types of urban land continues to erode natural and semi-natural ecological space and causes irreversible ecological damage to rapidly industrialized and urbanized areas. This work considers Quanzhou, a typical industrial and trade city in southeastern China as the research area and uses a Markov chain integrated into the patch-generating land use simulation (PLUS) model to simulate the urban expansion of Quanzhou from 2005 to 2018. The PLUS model uses the random forest algorithm to determine the contribution of driving factors and simulate the organic and spontaneous growth process based on the seed generation mechanism of multi-class random patches. Next, leveraging the importance of ecosystem services and ecological sensitivity as indicators of evaluation endpoints, we explore the temporal and spatial evolution of ecological risks from 2018 to 2031 under the scenarios of business as usual (BAU), industrial priority, and urban transformation scenarios. The evaluation endpoints cover water conservation service, soil conservation service, biodiversity maintenance service, soil erosion sensitivity, riverside sensitivity, and soil fertility. The ecological risk studied in this work involves the way in which different types of construction land expansion can possibly affect the ecosystem. The ecological risk index is divided into five levels. The results show that during the calibration simulation period from 2005 to 2018 the overall accuracy and Kappa coefficient reached 91.77% and 0.878, respectively. When the percent-of-seeds (PoS) parameter of random patch seeds equals 0.0001, the figure of merit of the simulated urban construction land improves by 3.9% compared with the logistic-based cellular automata model (Logistic-CA) considering organic growth. When PoS = 0.02, the figure of merit of the simulated industrial and mining land is 6.5% higher than that of the Logistic-CA model. The spatial reconstruction of multiple types of construction land under different urban development goals shows significant spatial differentiation on the district and county scale. In the industrial-priority scenario, the area of industrial and mining land is increased by 20% compared with the BAU scenario, but the high-level risk area is 42.5% larger than in the BAU scenario. Comparing the spatial distribution of risks under the BAU scenario, the urban transition scenario is mainly manifested as the expansion of medium-level risk areas around Quanzhou Bay and the southern region. In the future, the study area should appropriately reduce the agglomeration scale of urban development and increase the policy efforts to guide the development of industrial land to the southeast.